Abstract: DP (Clustering by Fast Search and Find of Density Peaks) is a novel clustering algorithm based on local density and distance between data,which has the advantages of being able to discover the clusters of arbitrary shapes,easy to accept logic principle and efficiently partitioning data into data sets.However,this algorithm has some disadvantages,for instance,it can’t deal with the case when multiple density peaks co-exist in a single cluster,and it is characterized with the unstable class label allocation.At the same time,it can’t accurately identify the clusters of sparse data when the density difference between clusters is big enough.In order to overcome the above deficiencies,this paper proposed a robust density peak clustering algorithm based on influence space.The proposed algorithm is capable of calculating local density by neighboring data and enhances the ability to recognize small-scale clusters.In order to improve the robustness of data partition,the data influence space is introduced,a new symmetric relationship is defined,and a new allocation strategy is proposed.By calculating the weighted density ratio of the target data to the adjacent data,the algorithm is able to process data sets with multiple density distribution features.The local density is obtained by calculating the density ratio of the target data to the neighbor data,and the ratio is weighted through using the size of the influence space.In order to verify the availability of the proposed algorithm,simulation experiments were conducted on synthetic data sets and UCI data sets,and NMI and Acc indexs were used to evaluate clustering algorithms.Simulation experiment results show that the proposed algorithm can reduce the influence of the cutoff distance on the algorithm and classify the data more stably.Compared with other algorithms,the proposed algorithm achieves better experimental results on both NMI and Acc indexs.

Key words: Allocation strategy, Clustering algorithm, Influence space, Local density, Robustness